Department of Orthopaedic Surgery, Faculty of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8655, Japan.
Department of Preventive Medicine for Locomotive Organ Disorders, 22Nd Century Medical and Research Center, Faculty of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8655, Japan.
Skeletal Radiol. 2024 Nov;53(11):2357-2366. doi: 10.1007/s00256-024-04618-2. Epub 2024 Mar 13.
We developed the deep neural network (DNN) model to automatically measure hallux valgus angle (HVA) and intermetatarsal angle (IMA) on foot radiographs. The objective is to assess the accuracy of the model by comparing to the manual measurement of foot and ankle surgeons.
A DNN was developed to predict the bone axes of the first proximal phalanx and all metatarsals from the first to the fifth in foot radiographs. The dataset used for model development consisted of 1798 radiographs collected from a population-based cohort and patients at our foot and ankle clinic. The retrospective validation cohort comprised of 92 radiographs obtained from 92 consecutive patients visiting our foot and ankle clinic. The mean absolute error (MAE) between automatic measurements by the model and the median of manual measurements by three foot and ankle surgeons was compared to 3° using one-tailed t-test and was also compared to the inter-rater difference in manual measurements among the three surgeons using two-tailed paired t-test.
The MAE for HVA was 1.3° (upper limit of 95% CI 1.6°), and this was significantly smaller than the inter-rater difference of 2.0 ± 0.2° among the surgeons, demonstrating the superior accuracy of the model. In contrast, the MAE for IMA was 0.8° (upper limit of 95% CI 1.0°) that showed no significant difference from the inter-rater difference of 1.0 ± 0.1° among the surgeons.
Our model demonstrated the ability to measure the HVA and IMA with an accuracy comparable to that of specialists.
我们开发了深度神经网络（DNN）模型，以自动测量足部 X 光片中的拇外翻角（HVA）和跖骨间角（IMA）。目的是通过与足部和踝关节外科医生的手动测量进行比较来评估该模型的准确性。
开发了一个 DNN 来预测足部 X 光片中第一近节趾骨和所有跖骨从第一到第五的骨轴。用于模型开发的数据集中包含了来自基于人群的队列和我们的足踝诊所患者的 1798 张 X 光片。回顾性验证队列由来自我们足踝诊所的 92 名连续患者的 92 张 X 光片组成。模型自动测量与三名足踝外科医生手动测量中位数之间的平均绝对误差（MAE）与单侧 t 检验相比为 3°，与三名外科医生手动测量中的组内差异相比也为双侧配对 t 检验。
HVA 的 MAE 为 1.3°（95%CI 上限为 1.6°），明显小于外科医生之间的组内差异 2.0±0.2°，表明模型的准确性更高。相比之下，IMA 的 MAE 为 0.8°（95%CI 上限为 1.0°），与外科医生之间的组内差异 1.0±0.1°无显著差异。
我们的模型证明了以与专家相当的准确性测量 HVA 和 IMA 的能力。
